code: def __init__(self, dim, heads=8, dim_head=64, use_flash=True):
      super().__init__()
      inner_dim = dim_head * heads
      self.heads = heads
      self.scale = dim_head**-0.5
      self.norm = nn.LayerNorm(dim)
  
      self.attend = Attend(use_flash=use_flash)
  
      self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
      self.to_out = nn.Linear(inner_dim, dim, bias=False)
  
imports: from collections import namedtuple
  from packaging import version
  import torch
  import torch.nn.functional
  from torch import nn
  from einops import rearrange
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, heads, dim_head, use_flash)